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Record W4285076423 · doi:10.1080/22423982.2022.2094532

Breaking trail in the Northwest Territories: a qualitative study of Indigenous Peoples’ experiences on the pathway to becoming a physician

2022· article· en· W4285076423 on OpenAlexafffundabout
Thomsen D’Hont, Kent Stobart, Susan Chatwood

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of TorontoInstitute for Circumpolar Health ResearchResearch CanadaUniversity of SaskatchewanUniversity of Alberta
FundersCanadian Institutes of Health ResearchGordon Foundation
KeywordsIndigenousQualitative researchGeographyCircumpolar starMedicineEthnologyHistorySociologyAnthropologyEcologyBiologyOceanography

Abstract

fetched live from OpenAlex

Currently, there is a lack of Indigenous physicians in the Northwest Territories (NWT), Canada. The goal of this qualitative study was to explore the underlying factors that influence the journey to becoming a medical doctor and returning home to practice for Indigenous students from the NWT. Eight qualitative, semi-structured interviews were conducted by phone or in-person. Participants represented Dene, Inuvialuit and Métis from the NWT and were at varying points in their journey into careers in medicine, from undergraduate university students through to practicing physicians. The main themes identified included access to high-school courses, the role of guidance counsellors, access to mentors and role models, a need to prioritise clinical experience in the NWT, influences of family and friends, diversity and inclusion, and finances. Interpretations: Significant barriers, some insurmountable, remain at every stage of the journey into medicine for aspiring Indigenous medical doctors from the NWT. These findings can inform policy development for pathway program that assist aspiring Indigenous physicians at each stage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.467
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes3
Has abstractyes

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